Using colocation to support human memory

The progress of health care in the western world has been marked by an increase in life expectancy. Advances in life expectancy have meant that more people are living with acute health problems, many of which are related to impairment of memory. This paper describes a pair of scenarios that use RFID to assist people who may suffer frommemory defects to extend their capability for independent living. We present our implementation of an RFID glove, describe its operation, and show how it enables the application scenarios

iForgot: a model of forgetting in robotic memories

Much effort has focused in recent years on developing more life-like robots. In this paper we propose a model of memory for robots, based on human digital memories, though our model incorporates an element of forgetting to ensure that the robotic memory appears more human and therefore can address some of the challenges for human-robot interaction

Memory-Augmented Temporal Dynamic Learning for Action Recognition

Human actions captured in video sequences contain two crucial factors for action recognition, i.e., visual appearance and motion dynamics. To model these two aspects, Convolutional and Recurrent Neural Networks (CNNs and RNNs) are adopted in most existing successful methods for recognizing actions. However, CNN based methods are limited in modeling long-term motion dynamics. RNNs are able to learn temporal motion dynamics but lack effective ways to tackle unsteady dynamics in long-duration motion. In this work, we propose a memory-augmented temporal dynamic learning network, which learns to write the most evident information into an external memory module and ignore irrelevant ones. In particular, we present a differential memory controller to make a discrete decision on whether the external memory module should be updated with current feature. The discrete memory controller takes in the memory history, context embedding and current feature as inputs and controls information flow into the external memory module. Additionally, we train this discrete memory controller using straight-through estimator. We evaluate this end-to-end system on benchmark datasets (UCF101 and HMDB51) of human action recognition. The experimental results show consistent improvements on both datasets over prior works and our baselines.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

Augmenting human memory using personal lifelogs

Memory is a key human facility to support life activities, including social interactions, life management and problem solving. Unfortunately, our memory is not perfect. Normal individuals will have occasional memory problems which can be frustrating, while those with memory impairments can often experience a greatly reduced quality of life. Augmenting memory has the potential to make normal individuals more effective, and those with significant memory problems to have a higher general quality of life. Current technologies are now making it possible to automatically capture and store daily life experiences over an extended period, potentially even over a lifetime. This type of data collection, often referred to as a personal life log (PLL), can include data such as continuously captured pictures or videos from a first person perspective, scanned copies of archival material such as books, electronic documents read or created, and emails and SMS messages sent and received, along with context data of time of capture and access and location via GPS sensors. PLLs offer the potential for memory augmentation. Existing work on PLLs has focused on the technologies of data capture and retrieval, but little work has been done to explore how these captured data and retrieval techniques can be applied to actual use by normal people in supporting their memory. In this paper, we explore the needs for augmenting human memory from normal people based on the psychology literature on mechanisms about memory problems, and discuss the possible functions that PLLs can provide to support these memory augmentation needs. Based on this, we also suggest guidelines for data for capture, retrieval needs and computer-based interface design. Finally we introduce our work-in-process prototype PLL search system in the iCLIPS project to give an example of augmenting human memory with PLLs and computer based interfaces

Who am I talking with? A face memory for social robots

In order to provide personalized services and to develop human-like interaction capabilities robots need to rec- ognize their human partner. Face recognition has been studied in the past decade exhaustively in the context of security systems and with significant progress on huge datasets. However, these capabilities are not in focus when it comes to social interaction situations. Humans are able to remember people seen for a short moment in time and apply this knowledge directly in their engagement in conversation. In order to equip a robot with capabilities to recall human interlocutors and to provide user- aware services, we adopt human-human interaction schemes to propose a face memory on the basis of active appearance models integrated with the active memory architecture. This paper presents the concept of the interactive face memory, the applied recognition algorithms, and their embedding into the robot’s system architecture. Performance measures are discussed for general face databases as well as scenario-specific datasets

An investigation into the effect of ageing on expert memory with CHREST

CHREST is a cognitive architecture that models human perception, learning, memory, and problem solving, and which has successfully simulated numerous human experimental data on chess. In this paper, we describe an investigation into the effects of ageing on expert memory using CHREST. The results of the simulations are related to the literature on ageing. The study illustrates how Computational Intelligence can be used to understand complex phenomena that are affected by multiple variables dynamically evolving as a function of time and that have direct practical implications for human societies

Integrating memory context into personal information re-finding

Personal information archives are emerging as a new challenge for information retrieval (IR) techniques. The user’s memory plays a greater role in retrieval from person archives than from other more traditional types of information collection (e.g. the Web), due to the large overlap of its content and individual human memory of the captured material. This paper presents a new analysis on IR of personal archives from a cognitive perspective. Some existing work on personal information management (PIM) has begun to employ human memory features into their IR systems. In our work we seek to go further, we assume that for IR in PIM system terms can be weighted not only by traditional IR methods, but also taking the user’s recall reliability into account. We aim to develop algorithms that combine factors from both the system side and the user side to achieve more effective searching. In this paper, we discuss possible applications of human memory theories for this algorithm, and present results from a pilot study and a proposed model of data structure for the HDMs achieves

Event-related brain potential correlates of human auditory sensory memory-trace formation

The event-related potential (ERP) component mismatch negativity (MMN) is a neural marker of human echoic memory. MMN is elicited by deviant sounds embedded in a stream of frequent standards, reflecting the deviation from an inferred memory trace of the standard stimulus. The strength of this memory trace is thought to be proportional to the number of repetitions of the standard tone, visible as the progressive enhancement of MMN with number of repetitions (MMN memory-trace effect). However, no direct ERP correlates of the formation of echoic memory traces are currently known. This study set out to investigate changes in ERPs to different numbers of repetitions of standards, delivered in a roving-stimulus paradigm in which the frequency of the standard stimulus changed randomly between stimulus trains. Normal healthy volunteers (n = 40) were engaged in two experimental conditions: during passive listening and while actively discriminating changes in tone frequency. As predicted, MMN increased with increasing number of standards. However, this MMN memory-trace effect was caused mainly by enhancement with stimulus repetition of a slow positive wave from 50 to 250 ms poststimulus in the standard ERP, which is termed here "repetition positivity" (RP). This RP was recorded from frontocentral electrodes when participants were passively listening to or actively discriminating changes in tone frequency. RP may represent a human ERP correlate of rapid and stimulus-specific adaptation, a candidate neuronal mechanism underlying sensory memory formation in the auditory cortex